Partner Influence Engine for Scalable Revenue Attribution
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Solution Overview
Problem
Existing methods for determining partner influence on sales revenue are inaccurate, unscalable, and labor-intensive, relying on manual attribution and anecdotal data, which fails to provide true attribution and scalability.
Innovation Solution
A system and method using a partner influence engine that collects and analyzes data from various sources to automatically determine and report the influence of partners on revenue, providing a non-binary assessment of their contribution to sales through a partner ecosystem platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual attribution tracking is used to determine partner influence, then the process can be performed with existing systems, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service tracking by automatically collecting interaction data from multiple sources (emails, calls, meetings) and computing partner influence without requiring manual intervention. The platform autonomously processes data, determines influence levels, and generates reports, eliminating the need for manual attribution tracking while reducing time consumption.
Solution Approach 2:
The patent replaces manual mechanical tracking processes with an automated electronic system that uses algorithms to analyze interaction data. Instead of manual review and calculation, the system uses computational methods to process data from multiple sources, determine partner influence, and generate attributions, thereby eliminating time-consuming manual operations.
2Ease of manufacture
If manual attribution tracking is used, then existing systems can be utilized, but the tracking is inaccurate and lacks true attribution
Solution Approach 1:
The system segments the attribution process into multiple independent components: data collection from various interaction sources, influence calculation based on weighted factors, and report generation. This segmentation allows each component to be optimized independently, improving overall accuracy while maintaining ease of implementation through modular architecture.
Solution Approach 2:
The patent changes the parameters of attribution tracking by moving from binary yes/no decisions to continuous influence scoring. The system calculates influence levels based on multiple parameters including interaction frequency, recency, and type of interaction, enabling precise measurement of partner contribution rather than simple manual attribution.
3Device complexity
If manual tracking methods are used, then the system complexity remains low, but the method is not scalable
Solution Approach 1:
The system achieves scalability through universality by designing a multi-functional platform that can handle increasing volumes of data and partners without proportionally increasing complexity. The same core algorithms and data collection mechanisms serve all partners and interactions, allowing the system to scale from small to large organizations while maintaining consistent performance.
Solution Approach 2:
The patent addresses scalability by changing the approach from manual processing to automated computational processing. The system uses efficient algorithms that process data in parallel and scale linearly with the amount of data, allowing it to handle large numbers of partners and interactions without requiring proportional increases in system complexity or manual intervention.
4Ease of manufacture
If manual attribution methods are used, then implementation is straightforward, but the process is exhausting and tedious
Solution Approach 1:
The system eliminates operational exhaustion by enabling self-service operation. The platform automatically collects data from interaction sources, processes it through influence calculation algorithms, and generates attribution reports without requiring manual intervention. This self-service approach maintains ease of implementation while dramatically improving operational ease by eliminating tedious manual tasks.
Data Source
AI summary
A system and method of attributing an influence that a partner has on an account, are described. The method includes receiving activity data indicating interactions between the partner and the account. The activity data is used to determine the influence that the partner has on the account. A report is transmitted for display, and the report includes influence data indicating the influence of the partner on the account. Other embodiments are also described and claimed.